Data for Tethered Gaussian wavepackets for quantum dynamics simulations : sticking together for better convergence

The effective `volume' of standard Gaussian products decreases as dimensionality $f$ increases, meaning that rapidly-growing basis sets are required to obtain accurate results as $f$ grows. Here, we show that the flexibility inherent in Gaussian-based simulations allows development of new basis sets in which behavior at higher-dimensionality is improved.

For the case of PES regression, we first show that recasting Gaussian basis functions in an `additive' form improves fitting convergence in higher-dimensional problems. Taking inspiration from these PES regression studies, we then propose two new Gaussian wavepacket (GWP) basis functions - stretched and tether GWPs - for quantum dynamics simulations. When combined with careful choice of initial GWP conditions, we find that tethered GWP basis sets, in particular, improve convergence of predicted time-dependent observables compared to standard product GWPs.